Hierarchical probabilistic Gabor and MRF segmentation of brain tumours in MRI volumes

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 8149 LNCS, Issue PART 1, 2013, Pages 751-758

  Subbanna, Nagesh K ^a   Precup, Doina ^a   Collins, D Louis ^b   Arbel, Tal ^a  

^a MCGILL UNIVERSITY   (Canada)

^b MCGILL UNIVERSITY   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN FRAMEWORKS; GABOR DECOMPOSITIONS; INTENSITY DIFFERENCE; MARKOV RANDOM FIELDS; PROBABILISTIC FRAMEWORK; STATE-OF-THE-ART TECHNIQUES; TEXTURE-BASED SEGMENTATIONS; TRANSITION PROBABILITIES;

BRAIN; IMAGE SEGMENTATION; MAGNETIC RESONANCE IMAGING; MARKOV PROCESSES;

TUMORS;

ALGORITHM; ARTICLE; AUTOMATED PATTERN RECOGNITION; BIOLOGICAL MODEL; BRAIN TUMOR; COMPUTER ASSISTED DIAGNOSIS; COMPUTER SIMULATION; HUMAN; IMAGE ENHANCEMENT; IMAGE SUBTRACTION; INFORMATION RETRIEVAL; METHODOLOGY; NUCLEAR MAGNETIC RESONANCE IMAGING; PATHOLOGY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; STATISTICAL ANALYSIS; STATISTICAL MODEL; THREE DIMENSIONAL IMAGING; TUMOR VOLUME;

ALGORITHMS; BRAIN NEOPLASMS; COMPUTER SIMULATION; DATA INTERPRETATION, STATISTICAL; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; IMAGING, THREE-DIMENSIONAL; INFORMATION STORAGE AND RETRIEVAL; MAGNETIC RESONANCE IMAGING; MODELS, NEUROLOGICAL; MODELS, STATISTICAL; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SUBTRACTION TECHNIQUE; TUMOR BURDEN;

EID: 84894615199     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-40811-3_94     Document Type: Conference Paper

References (19)

1. http://www2.imm.dtu.dk/projects/BRATS2012/data.html
2. Ferlay, J., et al.: Estimates of worldside burden of cancer in 2008. In: GLOBOCAN 2008 (2008)
3. Corso, J., et al.: Efficient Multilevel Brain Tumour Segmentation with Integrated Bayesian Model Classification. IEEE Trans. Med. Imag. 27(5), 629-640 (2008)
4. Kaus, M., et al.: Adaptive Template Moderated Brain Tumour Segmentation in MRI. In: Workshop Fuer Bildverarbeitung Fur Die Medizin, pp. 102-105 (1999)
5. Prastawa, M., et al.: A brain tumor segmentation framework based on outlier detection. Med. Image Ana. 8(3), 275-283 (2004)
6. Menze, B.H., van Leemput, K., Lashkari, D., Weber, M.-A., Ayache, N., Golland, P.: A generative model for brain tumor segmentation in multi-modal images. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010, Part II. LNCS, vol. 6362, pp. 151-159. Springer, Heidelberg (2010)
7. Zikic, D., Glocker, B., Konukoglu, E., Criminisi, A., Demiralp, C., Shotton, J., Thomas, O.M., Das, T., Jena, R., Price, S.J.: Decision forests for tissue-specific segmentation of highgrade gliomas in multi-channel MR. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part III. LNCS, vol. 7512, pp. 369-376. Springer, Heidelberg (2012)
8. Wels, M., Carneiro, G., Aplas, A., Huber, M., Hornegger, J., Comaniciu, D.: A discriminative model-constrained graph-cuts approach to fully automated pediatric brain tumor segmentation in 3D MRI. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008, Part I. LNCS, vol. 5241, pp. 67-75. Springer, Heidelberg (2008)
9. Moon, N., et al.: Model based brain and tumor segmentation. In: ICPR, vol. 1, pp. 528-531 (2002)
10. Bauer, S., et al.: Segmentation of Brain Tumour Images Based on Integrated Hierarchical Classification and Regularisation. In: BRATS MICCAI (2012)
11. Parisot, S.: Graph-based Detection, Segmentation and Characterisation of Brain Tumours. In: CVPR, pp. 988-995 (2012)
12. Mishra, R.: MRI based brain tumor detection using wavelet packet feature and Artificial Neural Networks. In: Int. Conf. and Work. on Emerging Trends in Tech., pp. 656-659 (2010)
13. Bauer, S., et al.: Atlas-Based Segmentation of Brain Tumor Images Using a Markov Random Field-Based Tumor Growth Model and Non-Rigid Registration. In: IEEE EMBS, pp. 4080-4083 (2010)
14. Farias, G., et al.: Brain Tumour Diagnosis with Wavelets and Support Vector Machines. In: Int. Conf. Intell. Systems and Knowledge Engg., pp. 1453-1459 (2008)
15. Subbanna, N., et al.: Probabilistic Gabor and Markov Random Fields Segmentation of Brain Tumours in MRI Volumes. In: BRATS MICCAI (2012)
16. Zibulski, M.: Discrete multiwindow Gabor-type transforms. IEEE Trans. Sig. Proc. 45(6), 1428-1442 (1997)
17. Jain, A., et al.: Unsupervised Texture segmentation using Gabor filters. Patt. Rcgn. 24(12), 1167-1186 (1991)
18. Subbanna, N., et al.: Existence Conditions for Non-Canonical Discrete Multiwindow Gabor Frames. IEEE Trans. Sig. Proc. 55(10), 5113-5117 (2007)
19. Duda, R., et al.: Pattern Classification. John Wiley and Sons (2000)